Validation of machine-learning model for first-trimester prediction of pre-eclampsia using cohort from PREVAL study.

OBJECTIVE: Effective first-trimester screening for pre-eclampsia (PE) can be achieved using a competing-risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine-learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations. METHODS: Previously, a machine-learning model derived with the use of a fully connected neural network for first-trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF) and pregnancy-associated plasma protein-A (PAPP-A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first-trimester PE validation (PREVAL) study, in which first-trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing-risks model. The performance of screening was assessed by examining the area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at a 10% screen-positive rate (SPR). These indices were compared with those derived from the application of the FMF competing-risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression. RESULTS: The DRs at 10% SPR for early, preterm and all PE with the machine-learning model were 84.4% (95% CI, 67.2-94.7%), 77.8% (95% CI, 66.4-86.7%) and 55.7% (95% CI, 49.0-62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864-0.975), 0.913 (95% CI, 0.882-0.944) and 0.846 (95% CI, 0.820-0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA-PI and PlGF); inclusion of PAPP-A did not provide significant improvement in DR. The machine-learning model had similar performance to that achieved by the FMF competing-risks model (DR at 10% SPR, 82.7% (95% CI, 69.6-95.8%) for early PE, 72.7% (95% CI, 62.9-82.6%) for preterm PE and 55.1% (95% CI, 48.8-61.4%) for all PE) without requiring specific adaptations to the population. CONCLUSIONS: A machine-learning model for first-trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

STATIN trial: predictive performance of competing-risks model in screening for pre-eclampsia at 35-37 weeks' gestation.

OBJECTIVE: To examine the predictive performance of a previously reported competing-risks model of screening for pre-eclampsia (PE) at 35-37 weeks' gestation by combinations of maternal risk factors, mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), serum placental growth factor (PlGF) and serum soluble fms-like tyrosine kinase-1 (sFlt-1) in a validation dataset derived from the screened population of the STATIN study. METHODS: This was a prospective third-trimester multicenter study of screening for PE in singleton pregnancies by means of a previously reported algorithm that combines maternal risk factors and biomarkers. Women in the high-risk group were invited to participate in a trial of pravastatin vs placebo, but the trial showed no evidence of an effect of pravastatin in the prevention of PE. Patient-specific risks of delivery with PE were calculated using the competing-risks model, and the performance of screening for PE by maternal risk factors alone and by various combinations of risk factors with MAP, UtA-PI, PlGF and sFlt-1 was assessed. The predictive performance of the model was examined by, first, the ability of the model to discriminate between the PE and no-PE groups using the area under the receiver-operating-characteristics curve (AUC) and the detection rate at a fixed false-positive rate of 10%, and, second, calibration by measurements of calibration slope and calibration-in-the-large. RESULTS: The study population of 29 677 pregnancies contained 653 that developed PE. In screening for PE by a combination of maternal risk factors, MAP, PlGF and sFlt-1 (triple test), the detection rate at a 10% false-positive rate was 79% (95% CI, 76-82%) and the results were consistent with the data used for developing the algorithm. Addition of UtA-PI did not improve the prediction provided by the triple test. The AUC for the triple test was 0.923 (95% CI, 0.913-0.932), demonstrating very high discrimination between affected and unaffected pregnancies. Similarly, the calibration slope was 0.875 (95% CI, 0.831-0.919), demonstrating good agreement between the predicted risk and observed incidence of PE. CONCLUSION: The competing-risks model provides an effective and reproducible method for third-trimester prediction of term PE. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Screening for trisomies 21 and 18 in a Spanish public hospital: from the combined test to the cell-free DNA test.

OBJECTIVE: To describe our experience in first-trimester screening for trisomies 21 and 18 firstly by the combined test alone and secondly by cell-free (cf) DNA testing contingent on the results from a previously performed combined test. METHODS: Women with singleton pregnancies attending Torrejon University Hospital in Madrid, Spain, from November 2011 to January 2016, were screened for trisomy (T)21 and T18 by the combined test at 11-13 weeks. Before the introduction of cfDNA testing, women at high risk (>1 in 250) were offered invasive testing (IT) and from January 2015 they were offered cfDNA test as well as IT. RESULTS: Combined test was performed in 6011 pregnancies. The risk was high in 202 (3.4%) cases. There was complete follow-up for 5507 (91.6%) pregnancies. Detection rate (DR) for T21 was 83.3% (15/18) and 100% (4/4) for T18. Additionally, 2/2 (100%) cases of T13 and 2/2 (100%) cases of triploidy were also detected. False positive rate (FPR) was 3.2% (174/5488). The introduction of this contingent model was followed by a 73% reduction on the IT rate in the high-risk group, from 76.3% to 20.8%. CONCLUSION: Contingent screening for trisomies 21 and 18 by cfDNA testing at 11-13 weeks is feasible and has a lower IT rate than combined testing alone.